Glaucoma Research Today is a free monthly online journal that collates and summarizes the latest research about Glaucoma, including details on cataracts, surgery, treatment, blindness. | ||||||||
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Comparison of methods to predict visual field progression in glaucoma.Nouri-Mahdavi K, Hoffman D, Ralli M, Caprioli J Glaucoma Division, Jules Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA. OBJECTIVE: To compare performance of pointwise linear regression, Glaucoma Change Probability Analysis (GCPA), and the Advanced Glaucoma Intervention Study (AGIS) method in predicting visual field progression in glaucoma. DESIGN: Longitudinal visual field data from AGIS. Proportion of progressing eyes and time to progression were the main outcome measures. One hundred fifty-six patients with 8 or more years of follow-up were included. Prediction of outcomes at 8 years was used to evaluate the performance of each method (pointwise linear regression, GCPA, and AGIS). RESULTS: Visual field progression at 8 years was detected in 35%, 31%, and 22% of patients by pointwise linear regression, GCPA, and the AGIS method, respectively. Baseline mean deviation was not different for nonprogressing vs progressing eyes for all methods (P > .05). Pointwise linear regression and GCPA had the highest pairwise concordance (kappa = 0.58 [SD, 0.07]). The false prediction rates at 4 and 8 years varied between 1% and 3%. Glaucoma Change Probability Analysis predicted final outcomes better than pointwise linear regression at 4 years (P = .001). CONCLUSIONS: All algorithms had low false prediction rates. Glaucoma Change Probability Analysis predicted outcomes better than pointwise linear regression early during follow-up. Algorithms did not perform differently as a function of baseline damage. Pointwise linear regression and GCPA did not agree well regarding spatial distribution of worsening test locations. Published 11 September 2007 in Arch Ophthalmol, 125(9): 1176-81.
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